68 citations · 92 across the 3 of their papers we have counts for
7 papers
Hardware implementation of Bayesian network building blocks with stochastic spintronic devices
Punyashloka Debashis, Vaibhav Ostwal, Rafatul Faria +3
Bayesian networks are powerful statistical models to understand causal relationships in real-world probabilistic problems such as diagnosis, forecasting, computer vision, etc. For…
Hardware Design for Autonomous Bayesian Networks
Rafatul Faria, Jan Kaiser, Kerem Y. Camsari +1
Directed acyclic graphs or Bayesian networks that are popular in many AI related sectors for probabilistic inference and causal reasoning can be mapped to probabilistic circuits bu…
Correlated fluctuations in spin orbit torque-coupled perpendicular nanomagnets
Punyashloka Debashis, Rafatul Faria, Kerem Y. Camsari +2
Low barrier nanomagnets have attracted a lot of research interest for their use as sources of high quality true random number generation. More recently, low barrier nanomagnets wit…
Probabilistic Circuits for Autonomous Learning: A simulation study
Jan Kaiser, Rafatul Faria, Kerem Y. Camsari +1
Modern machine learning is based on powerful algorithms running on digital computing platforms and there is great interest in accelerating the learning process and making it more e…
Autonomous Probabilistic Coprocessing with Petaflips per Second
Brian Sutton, Rafatul Faria, Lakshmi A. Ghantasala +3
In this paper we present a concrete design for a probabilistic (p-) computer based on a network of p-bits, robust classical entities fluctuating between -1 and +1, with probabiliti…
Low Barrier Magnet Design for Efficient Hardware Binary Stochastic Neurons
Orchi Hassan, Rafatul Faria, Kerem Y. Camsari +2
Binary stochastic neurons (BSN's) form an integral part of many machine learning algorithms, motivating the development of hardware accelerators for this complex function. It has b…